Learning Machines: Foundations of Trainable Pattern-classifying Systems |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 14
Sivu 42
12 Widrow , B .: Generalization and Information Storage in Networks of Adaline " Neurons , " in Yovits , Jacobi , and Goldstein ( eds . ) , " Self - organizing Systems -1962 , " p . 442 , Spartan Books , Washington , D.C. , 1962 .
12 Widrow , B .: Generalization and Information Storage in Networks of Adaline " Neurons , " in Yovits , Jacobi , and Goldstein ( eds . ) , " Self - organizing Systems -1962 , " p . 442 , Spartan Books , Washington , D.C. , 1962 .
Sivu 95
CHAPTER 6 LAYERED MACHINES 6.1 Layered networks of TLUS Networks of interconnected TLUS have often been proposed as pattern- classifying machines . In these networks the binary responses of some TLUS are used as inputs to other TLUs .
CHAPTER 6 LAYERED MACHINES 6.1 Layered networks of TLUS Networks of interconnected TLUS have often been proposed as pattern- classifying machines . In these networks the binary responses of some TLUS are used as inputs to other TLUs .
Sivu 113
6.8 Bibliographical and historical remarks 1 2 3 4 Pattern - classifying TLU networks have been studied by Farley and Clark , Rosenblatt , Widrow , Brain et al . , and others . The simple a perceptron proposed by Rosenblatt is a two ...
6.8 Bibliographical and historical remarks 1 2 3 4 Pattern - classifying TLU networks have been studied by Farley and Clark , Rosenblatt , Widrow , Brain et al . , and others . The simple a perceptron proposed by Rosenblatt is a two ...
Mitä ihmiset sanovat - Kirjoita arvostelu
Yhtään arvostelua ei löytynyt.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |